This semester, the learning seminar on mathematics behind machine learning will be held on Thursdays, 12:45-1:45 at BSB 417 (with one exception on Monday, March 10). We will continue studying the book, Understanding Deep Learning by Simon Prince (you can download the book from https://udlbook.github.io/udlbook/). There will also be outside experts speaking on various topics on AI in our seminar (see below for the tentative schedule). If you are interested in participating in the seminar, please let us know so that we can add you to the seminar Canvas site. You are welcome to join us either in-person or via Zoom (please email Dr. Fu for the Zoom link).

Seminar Organizers: Siqi Fu, Benedetto Piccoli, and Longmei Shu

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Tentative Schedule:

02/13/2025 Siqi Fu: Chapter 10-Convolutional Networks
02/20/2025 Kevin Quan: Chapter 11-Residual Networks
02/27/2025 Sunil Shende: Chapter 12-Transformers
03/06/2025 Lucas Liona: Chapter 13-Graph Neural Networks
03/10/2025 Benoit Dherin (Google): Why Neural Networks Find Simple Solutions? (Special time and location: 11:20-12:20 at Armitage 124)
03/27/2025 Gehrig Rios: Chapter 14-Unsupervised Learning
04/10/2025 Longmei Shu: Chapter 15-Generative Adversarial Networks
04/17/2025 Benedetto Piccoli: Chapter 16-Normalizing Flows
04/24/2025 Haibin Ling (Stony Brook): TBA
05/01/2025 Mark Bolding (Georgia Tech Research Institute): TBA
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